{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Basketball Data Exploration",
      "version": "0.3.2",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "lCx4HrVxoq_1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# This code explores the NBA players from 2013 - 2014 basketball season, and uses\n",
        "# a machine learning algorithm called kMeans to group them in clusters, this will\n",
        "# show which players are most similar\n",
        "\n",
        "#Stat Glossaries: https://www.basketball-reference.com/about/glossary.html\n",
        "#                 https://stats.nba.com/help/glossary/#fta\n",
        "\n",
        "#Resource: https://www.dataquest.io/blog/python-vs-r/\n",
        "\n",
        "#import the dependencies\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jBT0TBXAkDy0",
        "colab_type": "code",
        "outputId": "fcd8b837-8525-43b5-f9d8-6b87e77406dd",
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": 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              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 450
        }
      },
      "source": [
        "#load the data \n",
        "#from google.colab import files #Only use for Google Colab\n",
        "#uploaded = files.upload()      #Only use for Google Colab\n",
        "nba = pd.read_csv('nba_2013.csv')# the nba_2013.csv data contains data on NBA players from 2013 - 2014 season\n",
        "nba.head(7)# Print the first 7 rows of data or first 7 players"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-e4c6dd6b-01bc-4cc5-ae5d-158ec79c7e57\" name=\"files[]\" multiple disabled />\n",
              "     <output id=\"result-e4c6dd6b-01bc-4cc5-ae5d-158ec79c7e57\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving nba_2013.csv to nba_2013 (2).csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>player</th>\n",
              "      <th>pos</th>\n",
              "      <th>age</th>\n",
              "      <th>bref_team_id</th>\n",
              "      <th>g</th>\n",
              "      <th>gs</th>\n",
              "      <th>mp</th>\n",
              "      <th>fg</th>\n",
              "      <th>fga</th>\n",
              "      <th>fg.</th>\n",
              "      <th>x3p</th>\n",
              "      <th>x3pa</th>\n",
              "      <th>x3p.</th>\n",
              "      <th>x2p</th>\n",
              "      <th>x2pa</th>\n",
              "      <th>x2p.</th>\n",
              "      <th>efg.</th>\n",
              "      <th>ft</th>\n",
              "      <th>fta</th>\n",
              "      <th>ft.</th>\n",
              "      <th>orb</th>\n",
              "      <th>drb</th>\n",
              "      <th>trb</th>\n",
              "      <th>ast</th>\n",
              "      <th>stl</th>\n",
              "      <th>blk</th>\n",
              "      <th>tov</th>\n",
              "      <th>pf</th>\n",
              "      <th>pts</th>\n",
              "      <th>season</th>\n",
              "      <th>season_end</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Quincy Acy</td>\n",
              "      <td>SF</td>\n",
              "      <td>23</td>\n",
              "      <td>TOT</td>\n",
              "      <td>63</td>\n",
              "      <td>0</td>\n",
              "      <td>847</td>\n",
              "      <td>66</td>\n",
              "      <td>141</td>\n",
              "      <td>0.468</td>\n",
              "      <td>4</td>\n",
              "      <td>15</td>\n",
              "      <td>0.266667</td>\n",
              "      <td>62</td>\n",
              "      <td>126</td>\n",
              "      <td>0.492063</td>\n",
              "      <td>0.482</td>\n",
              "      <td>35</td>\n",
              "      <td>53</td>\n",
              "      <td>0.660</td>\n",
              "      <td>72</td>\n",
              "      <td>144</td>\n",
              "      <td>216</td>\n",
              "      <td>28</td>\n",
              "      <td>23</td>\n",
              "      <td>26</td>\n",
              "      <td>30</td>\n",
              "      <td>122</td>\n",
              "      <td>171</td>\n",
              "      <td>2013-2014</td>\n",
              "      <td>2013</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Steven Adams</td>\n",
              "      <td>C</td>\n",
              "      <td>20</td>\n",
              "      <td>OKC</td>\n",
              "      <td>81</td>\n",
              "      <td>20</td>\n",
              "      <td>1197</td>\n",
              "      <td>93</td>\n",
              "      <td>185</td>\n",
              "      <td>0.503</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>93</td>\n",
              "      <td>185</td>\n",
              "      <td>0.502703</td>\n",
              "      <td>0.503</td>\n",
              "      <td>79</td>\n",
              "      <td>136</td>\n",
              "      <td>0.581</td>\n",
              "      <td>142</td>\n",
              "      <td>190</td>\n",
              "      <td>332</td>\n",
              "      <td>43</td>\n",
              "      <td>40</td>\n",
              "      <td>57</td>\n",
              "      <td>71</td>\n",
              "      <td>203</td>\n",
              "      <td>265</td>\n",
              "      <td>2013-2014</td>\n",
              "      <td>2013</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Jeff Adrien</td>\n",
              "      <td>PF</td>\n",
              "      <td>27</td>\n",
              "      <td>TOT</td>\n",
              "      <td>53</td>\n",
              "      <td>12</td>\n",
              "      <td>961</td>\n",
              "      <td>143</td>\n",
              "      <td>275</td>\n",
              "      <td>0.520</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>143</td>\n",
              "      <td>275</td>\n",
              "      <td>0.520000</td>\n",
              "      <td>0.520</td>\n",
              "      <td>76</td>\n",
              "      <td>119</td>\n",
              "      <td>0.639</td>\n",
              "      <td>102</td>\n",
              "      <td>204</td>\n",
              "      <td>306</td>\n",
              "      <td>38</td>\n",
              "      <td>24</td>\n",
              "      <td>36</td>\n",
              "      <td>39</td>\n",
              "      <td>108</td>\n",
              "      <td>362</td>\n",
              "      <td>2013-2014</td>\n",
              "      <td>2013</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Arron Afflalo</td>\n",
              "      <td>SG</td>\n",
              "      <td>28</td>\n",
              "      <td>ORL</td>\n",
              "      <td>73</td>\n",
              "      <td>73</td>\n",
              "      <td>2552</td>\n",
              "      <td>464</td>\n",
              "      <td>1011</td>\n",
              "      <td>0.459</td>\n",
              "      <td>128</td>\n",
              "      <td>300</td>\n",
              "      <td>0.426667</td>\n",
              "      <td>336</td>\n",
              "      <td>711</td>\n",
              "      <td>0.472574</td>\n",
              "      <td>0.522</td>\n",
              "      <td>274</td>\n",
              "      <td>336</td>\n",
              "      <td>0.815</td>\n",
              "      <td>32</td>\n",
              "      <td>230</td>\n",
              "      <td>262</td>\n",
              "      <td>248</td>\n",
              "      <td>35</td>\n",
              "      <td>3</td>\n",
              "      <td>146</td>\n",
              "      <td>136</td>\n",
              "      <td>1330</td>\n",
              "      <td>2013-2014</td>\n",
              "      <td>2013</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Alexis Ajinca</td>\n",
              "      <td>C</td>\n",
              "      <td>25</td>\n",
              "      <td>NOP</td>\n",
              "      <td>56</td>\n",
              "      <td>30</td>\n",
              "      <td>951</td>\n",
              "      <td>136</td>\n",
              "      <td>249</td>\n",
              "      <td>0.546</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>136</td>\n",
              "      <td>248</td>\n",
              "      <td>0.548387</td>\n",
              "      <td>0.546</td>\n",
              "      <td>56</td>\n",
              "      <td>67</td>\n",
              "      <td>0.836</td>\n",
              "      <td>94</td>\n",
              "      <td>183</td>\n",
              "      <td>277</td>\n",
              "      <td>40</td>\n",
              "      <td>23</td>\n",
              "      <td>46</td>\n",
              "      <td>63</td>\n",
              "      <td>187</td>\n",
              "      <td>328</td>\n",
              "      <td>2013-2014</td>\n",
              "      <td>2013</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Cole Aldrich</td>\n",
              "      <td>C</td>\n",
              "      <td>25</td>\n",
              "      <td>NYK</td>\n",
              "      <td>46</td>\n",
              "      <td>2</td>\n",
              "      <td>330</td>\n",
              "      <td>33</td>\n",
              "      <td>61</td>\n",
              "      <td>0.541</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>33</td>\n",
              "      <td>61</td>\n",
              "      <td>0.540984</td>\n",
              "      <td>0.541</td>\n",
              "      <td>26</td>\n",
              "      <td>30</td>\n",
              "      <td>0.867</td>\n",
              "      <td>37</td>\n",
              "      <td>92</td>\n",
              "      <td>129</td>\n",
              "      <td>14</td>\n",
              "      <td>8</td>\n",
              "      <td>30</td>\n",
              "      <td>18</td>\n",
              "      <td>40</td>\n",
              "      <td>92</td>\n",
              "      <td>2013-2014</td>\n",
              "      <td>2013</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>LaMarcus Aldridge</td>\n",
              "      <td>PF</td>\n",
              "      <td>28</td>\n",
              "      <td>POR</td>\n",
              "      <td>69</td>\n",
              "      <td>69</td>\n",
              "      <td>2498</td>\n",
              "      <td>652</td>\n",
              "      <td>1423</td>\n",
              "      <td>0.458</td>\n",
              "      <td>3</td>\n",
              "      <td>15</td>\n",
              "      <td>0.200000</td>\n",
              "      <td>649</td>\n",
              "      <td>1408</td>\n",
              "      <td>0.460938</td>\n",
              "      <td>0.459</td>\n",
              "      <td>296</td>\n",
              "      <td>360</td>\n",
              "      <td>0.822</td>\n",
              "      <td>166</td>\n",
              "      <td>599</td>\n",
              "      <td>765</td>\n",
              "      <td>178</td>\n",
              "      <td>63</td>\n",
              "      <td>68</td>\n",
              "      <td>123</td>\n",
              "      <td>147</td>\n",
              "      <td>1603</td>\n",
              "      <td>2013-2014</td>\n",
              "      <td>2013</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              player pos  age bref_team_id  ...   pf   pts     season  season_end\n",
              "0         Quincy Acy  SF   23          TOT  ...  122   171  2013-2014        2013\n",
              "1       Steven Adams   C   20          OKC  ...  203   265  2013-2014        2013\n",
              "2        Jeff Adrien  PF   27          TOT  ...  108   362  2013-2014        2013\n",
              "3      Arron Afflalo  SG   28          ORL  ...  136  1330  2013-2014        2013\n",
              "4      Alexis Ajinca   C   25          NOP  ...  187   328  2013-2014        2013\n",
              "5       Cole Aldrich   C   25          NYK  ...   40    92  2013-2014        2013\n",
              "6  LaMarcus Aldridge  PF   28          POR  ...  147  1603  2013-2014        2013\n",
              "\n",
              "[7 rows x 31 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1qHm6wj_pAZs",
        "colab_type": "code",
        "outputId": "1edf9063-0797-41b0-a88b-ff344f41a493",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "#Get the number of rows and columns (481 rows or players , and 31 columns containing data on the players)\n",
        "nba.shape"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(481, 31)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tw0E2wJcpZIt",
        "colab_type": "code",
        "outputId": "fd8dde62-ac04-49af-dd0e-33a6d3704036",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        }
      },
      "source": [
        "# Find the average value for each numeric column / feature\n",
        "nba.mean() # The columns have names like 'fg' (field goals made), 'ast'(assists), here is a glossary of all of the stats https://stats.nba.com/help/glossary/"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "age             26.509356\n",
              "g               53.253638\n",
              "gs              25.571726\n",
              "mp            1237.386694\n",
              "fg             192.881497\n",
              "fga            424.463617\n",
              "fg.              0.436436\n",
              "x3p             39.613306\n",
              "x3pa           110.130977\n",
              "x3p.             0.285111\n",
              "x2p            153.268191\n",
              "x2pa           314.332640\n",
              "x2p.             0.466947\n",
              "efg.             0.480752\n",
              "ft              91.205821\n",
              "fta            120.642412\n",
              "ft.              0.722419\n",
              "orb             55.810811\n",
              "drb            162.817048\n",
              "trb            218.627859\n",
              "ast            112.536383\n",
              "stl             39.280665\n",
              "blk             24.103950\n",
              "tov             71.862786\n",
              "pf             105.869023\n",
              "pts            516.582121\n",
              "season_end    2013.000000\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KpmWa388qJv0",
        "colab_type": "code",
        "outputId": "553876c6-bd33-42fb-bf16-13d83dec52e2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "#Get the mean / average of specific columns\n",
        "# mean of the specific column\n",
        "nba.loc[:,\"fg\"].mean()"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "192.88149688149687"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NfS18lB0qpbc",
        "colab_type": "code",
        "outputId": "3364327d-dbb9-4c62-bc42-ccb7baed841f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 549
        }
      },
      "source": [
        "#Make pairwise scatter plots\n",
        "# This is a common way to explore a data set to see how different columns correlate\n",
        "# to others, we'll compare ast (assits), fg(field goals), trb (total rebound )\n",
        "#NOTE: pairplot of all columns ==> sns.pairplot(nba)\n",
        "sns.pairplot(nba[[\"ast\", \"fg\", \"trb\"]])\n",
        "plt.show()\n",
        "\n"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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jasLOeEMpiF0n+DN7eMqNslMB+O1p+Z5jWPOjW1F+sEquYwEAx6ub0OryKpqL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8vO547J53LwaBP3vPghVfV+q85C2kAFoeM5kbkiILNF2pJgMExVvR9fIJy28N3UoSjI9bB8Vgl1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\nAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 540x540 with 12 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IHa9iJ55ledg",
        "colab_type": "code",
        "outputId": "582c56c1-f386-4197-9764-32a9b5f86a6c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        }
      },
      "source": [
        "#Make Heat Maps to see correlations\n",
        "#Note: Heat Map of all columns ==> sns.heatmap(nba.corr())\n",
        "#Note: Heat Map of all columns with annotation/numbers ==> sns.heatmap(nba.corr(), annot=True)\n",
        "#Heat Map of the columns ast (assits), fg(field goals), trb (total rebound ) with annotation\n",
        "correlation = nba[[\"ast\", \"fg\", \"trb\"]].corr()\n",
        "sns.heatmap(correlation, annot=True)"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fbe29976da0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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6pcAXZpYFhOdv+rI4m6c8RPfH/g2yYnz1zHyK13xO7s/HsvfD1ex65W0gWR3PrVwd95h+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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VYi0_zf3rEiu",
        "colab_type": "code",
        "outputId": "3c029f2a-c82f-4790-bd30-00ea0ca3bdc8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 403
        }
      },
      "source": [
        "# Make clusters of the players using a machine learning model called kMeans\n",
        "#One good way to explore this kind of data is to generate cluster plots.\n",
        "#This will show which players are most similar.\n",
        "\n",
        "from sklearn.cluster import KMeans\n",
        "kmeans_model = KMeans(n_clusters=5, random_state=1)# Create a 5 cluster kmeans model\n",
        "good_columns = nba._get_numeric_data().dropna(axis=1)#remove any non-numeric columns, or columns with missing values (NA, Nan, etc).\n",
        "kmeans_model.fit(good_columns)# Train the model\n",
        "labels = kmeans_model.labels_ # Get the labels or (cluster label for each player)\n",
        "labels\n"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 0, 0, 3, 0, 2, 3, 0, 4, 4, 4, 2, 4, 4, 1, 0, 4, 3, 2, 0, 1, 2,\n",
              "       0, 0, 1, 2, 0, 2, 2, 2, 4, 4, 1, 4, 2, 1, 4, 3, 4, 2, 0, 3, 0, 1,\n",
              "       0, 4, 2, 2, 0, 4, 4, 4, 4, 2, 2, 4, 0, 4, 1, 3, 1, 4, 1, 2, 4, 2,\n",
              "       2, 2, 2, 0, 2, 1, 1, 4, 1, 2, 2, 0, 0, 0, 1, 4, 2, 1, 1, 3, 4, 1,\n",
              "       4, 1, 2, 2, 0, 2, 2, 4, 2, 1, 0, 3, 2, 3, 2, 2, 1, 1, 0, 4, 2, 2,\n",
              "       3, 4, 2, 2, 2, 3, 0, 4, 2, 2, 2, 0, 1, 3, 1, 0, 0, 0, 3, 3, 4, 1,\n",
              "       1, 3, 2, 2, 3, 2, 0, 0, 1, 1, 0, 2, 1, 2, 4, 2, 4, 4, 1, 2, 2, 0,\n",
              "       1, 0, 0, 0, 1, 1, 3, 0, 3, 1, 4, 2, 2, 2, 2, 2, 1, 3, 0, 2, 4, 4,\n",
              "       1, 3, 0, 3, 2, 0, 2, 0, 4, 3, 4, 2, 2, 0, 2, 2, 2, 1, 0, 1, 0, 3,\n",
              "       1, 0, 1, 1, 1, 4, 1, 4, 2, 1, 0, 2, 2, 0, 3, 0, 4, 3, 4, 4, 3, 2,\n",
              "       1, 1, 2, 2, 2, 3, 2, 2, 2, 3, 1, 2, 3, 0, 1, 0, 0, 3, 2, 1, 2, 2,\n",
              "       0, 2, 1, 1, 0, 0, 1, 2, 4, 4, 0, 3, 1, 4, 2, 2, 0, 4, 2, 2, 1, 2,\n",
              "       1, 3, 2, 1, 2, 0, 0, 2, 3, 1, 4, 0, 1, 3, 3, 2, 4, 0, 1, 4, 0, 0,\n",
              "       1, 2, 3, 2, 2, 4, 0, 0, 2, 2, 1, 1, 1, 2, 1, 0, 0, 0, 4, 0, 4, 3,\n",
              "       2, 2, 3, 4, 2, 4, 1, 4, 0, 2, 4, 2, 2, 2, 2, 2, 2, 2, 0, 2, 1, 0,\n",
              "       3, 2, 3, 2, 0, 0, 2, 1, 4, 2, 2, 0, 4, 2, 1, 3, 4, 1, 1, 0, 1, 2,\n",
              "       4, 1, 2, 2, 2, 0, 2, 2, 0, 4, 2, 2, 2, 3, 0, 2, 0, 4, 2, 4, 0, 0,\n",
              "       0, 2, 1, 1, 2, 0, 4, 0, 0, 4, 4, 4, 0, 1, 2, 2, 4, 0, 0, 1, 2, 2,\n",
              "       2, 2, 2, 0, 1, 0, 3, 0, 2, 0, 4, 2, 3, 0, 2, 4, 1, 1, 0, 2, 3, 2,\n",
              "       4, 0, 2, 2, 2, 3, 2, 2, 4, 4, 3, 1, 4, 2, 0, 1, 0, 2, 3, 2, 0, 0,\n",
              "       1, 4, 2, 4, 0, 2, 1, 2, 1, 1, 3, 3, 4, 2, 0, 2, 2, 1, 3, 4, 2, 2,\n",
              "       1, 4, 0, 4, 4, 4, 2, 0, 0, 4, 2, 0, 2, 0, 1, 1, 3, 4, 0],\n",
              "      dtype=int32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qUk-ydWmtDxZ",
        "colab_type": "code",
        "outputId": "1a1878aa-8f8c-46c0-840b-b46697be0a98",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        }
      },
      "source": [
        "#Plot players by cluster\n",
        "#We can now plot out the players by cluster to discover patterns. \n",
        "#One way to do this is to first use PCA to make our data 2-dimensional, \n",
        "#then plot it, and shade each point according to cluster association\n",
        "\n",
        "#NOTE: PCA Principal component analysis (PCA) is a statistical procedure that uses an orthogonal transformation to convert a set of \n",
        "#observations of possibly correlated variables (entities each of which takes on various numerical values) into a set of values of linearly \n",
        "#uncorrelated variables called principal components. A Dimensionality Reducing Algorithm\n",
        "\n",
        "from sklearn.decomposition import PCA\n",
        "pca_2 = PCA(2)\n",
        "plot_columns = pca_2.fit_transform(good_columns)\n",
        "plt.scatter(x=plot_columns[:,0], y=plot_columns[:,1], c=labels)\n",
        "plt.show()"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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pHtsNi7CEfZ4TDaUIFIpjTHGpi1sfmUxeQSlOlxeHw8p7XyzkjQmj6diu/ntvGNJgw4GN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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2AUmtELaTbXX",
        "colab_type": "code",
        "outputId": "1b0ccf9b-7fc5-40ee-fa9d-6addcb1875c5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "#Show plot points\n",
        "plot_columns"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[-6.27969797e+02, -1.30839821e+02],\n",
              "       [-2.44268597e+02, -2.04096273e+02],\n",
              "       [-3.38828831e+02,  5.78889920e+01],\n",
              "       [ 1.74969229e+03,  5.08746550e+01],\n",
              "       [-3.83105785e+02,  4.99993062e+00],\n",
              "       [-1.13423594e+03,  6.09155972e+01],\n",
              "       [ 2.25796631e+03,  8.72646912e+02],\n",
              "       [-2.74018472e+02, -4.91584788e+01],\n",
              "       [ 6.51594149e+02, -3.68598261e+02],\n",
              "       [ 2.80257375e+01,  4.17702838e+00],\n",
              "       [ 7.72722373e+02, -2.88996107e+02],\n",
              "       [-1.30782656e+03,  7.78270023e+01],\n",
              "       [ 7.46758527e+01, -1.13379495e+02],\n",
              "       [ 4.27602950e+02, -3.54396243e+02],\n",
              "       [ 1.11065367e+03, -4.05383385e+02],\n",
              "       [-4.74415284e+02,  9.09308787e+01],\n",
              "       [ 5.49938152e+02, -3.57592577e+02],\n",
              "       [ 2.90757493e+03,  7.46898936e+02],\n",
              "       [-1.31941705e+03,  6.64245173e+01],\n",
              "       [-4.21874643e+02, -7.17577224e+01],\n",
              "       [ 1.68210924e+03, -4.08218490e+02],\n",
              "       [-1.39428553e+03,  1.10713700e+02],\n",
              "       [-1.22831506e+02, -1.78978791e+01],\n",
              "       [-4.05995463e+02, -4.28044333e+01],\n",
              "       [ 8.68584804e+02, -1.21791154e+02],\n",
              "       [-1.02445787e+03,  4.27439715e+01],\n",
              "       [-4.75016928e+02, -1.07997035e+02],\n",
              "       [-1.37622126e+03,  6.91689964e+01],\n",
              "       [-9.63645894e+02,  8.92252110e+00],\n",
              "       [-1.06102601e+03,  9.46126149e+01],\n",
              "       [ 3.59710212e+02,  1.14806418e+01],\n",
              "       [ 7.31677817e+01,  7.58890486e+01],\n",
              "       [ 1.00983705e+03, -2.79054575e+02],\n",
              "       [ 4.77739141e+02, -2.78882782e+02],\n",
              "       [-1.01801960e+03,  1.09139497e+02],\n",
              "       [ 1.25155645e+03,  3.98790063e+00],\n",
              "       [-7.03296602e+01, -4.88979321e+02],\n",
              "       [ 1.89040965e+03, -5.27235382e+02],\n",
              "       [ 4.99192171e+02, -1.26804839e+02],\n",
              "       [-9.12422725e+02,  8.21915609e+01],\n",
              "       [-3.81354756e+02,  3.04892052e+01],\n",
              "       [ 1.75040443e+03,  5.43064560e+01],\n",
              "       [-3.99772690e+02,  1.67481750e+02],\n",
              "       [ 9.18163998e+02, -1.63883835e+02],\n",
              "       [-7.21490812e+02,  3.37876203e+01],\n",
              "       [ 4.20643088e+02, -3.50985173e+02],\n",
              "       [-1.46354368e+03,  1.08490058e+02],\n",
              "       [-1.18364245e+03,  1.87848769e+01],\n",
              "       [-3.86697020e+02, -1.83590547e+02],\n",
              "       [ 1.33431524e+01,  8.31699445e+01],\n",
              "       [ 6.49092579e+01, -3.97181653e+02],\n",
              "       [ 6.52064540e+02,  2.37883041e+02],\n",
              "       [ 3.50437516e+02,  1.39566679e+02],\n",
              "       [-1.48764951e+03,  1.26531718e+02],\n",
              "       [-1.45365196e+03,  1.03812865e+02],\n",
              "       [ 5.02738807e+02, -2.13739114e+02],\n",
              "       [-7.68200493e+02, -9.27570215e+01],\n",
              "       [ 2.70987555e+02, -1.13865843e+02],\n",
              "       [ 1.41050952e+03,  3.66507250e+02],\n",
              "       [ 1.74642309e+03,  9.91746631e+01],\n",
              "       [ 8.70895746e+02,  8.84461422e+01],\n",
              "       [ 9.21899061e+01, -1.30122115e+02],\n",
              "       [ 1.51263160e+03, -2.82936028e+02],\n",
              "       [-1.36669250e+03,  4.65518787e+01],\n",
              "       [ 3.58645674e+02, -1.27677809e+02],\n",
              "       [-1.09703956e+03,  1.27160291e+02],\n",
              "       [-1.24490972e+03,  8.44971828e+01],\n",
              "       [-1.23220632e+03,  7.44361187e+01],\n",
              "       [-1.27593885e+03,  1.26132612e+02],\n",
              "       [-6.39879842e+02, -2.84031544e+01],\n",
              "       [-1.07489893e+03,  1.02391414e+01],\n",
              "       [ 1.21989715e+03, -2.03892889e+02],\n",
              "       [ 1.29111156e+03,  1.16297712e+02],\n",
              "       [ 2.04137015e+02, -9.73729667e+01],\n",
              "       [ 1.39724887e+03, -4.07557743e+02],\n",
              "       [-1.08362965e+03,  2.85319707e+01],\n",
              "       [-1.33997572e+03,  9.64342977e+01],\n",
              "       [-8.26323594e+02,  1.61029151e+02],\n",
              "       [-1.58997278e+02,  1.07308062e+02],\n",
              "       [-1.97423320e+02, -1.35313195e+02],\n",
              "       [ 1.27293084e+03, -5.17412893e+02],\n",
              "       [ 2.23536350e+02, -2.84411246e+02],\n",
              "       [-1.21188142e+03,  7.24040414e+01],\n",
              "       [ 1.13479111e+03, -3.99324505e+02],\n",
              "       [ 9.82956393e+02, -6.28657174e+01],\n",
              "       [ 1.68168250e+03,  1.77877476e+02],\n",
              "       [ 5.99310308e+00, -9.62152335e+01],\n",
              "       [ 9.09185527e+02, -4.23515741e+02],\n",
              "       [ 3.46574701e+02, -2.01392199e+02],\n",
              "       [ 8.43986582e+02, -1.42324339e+02],\n",
              "       [-1.48574711e+03,  1.14772327e+02],\n",
              "       [-1.27660445e+03,  8.73802940e+01],\n",
              "       [-6.22087925e+02, -3.86685900e+01],\n",
              "       [-1.29947350e+03,  9.72805080e+01],\n",
              "       [-1.30754737e+03,  7.54456948e+01],\n",
              "       [ 6.31960732e+02, -4.64454347e+02],\n",
              "       [-1.34009480e+03,  6.09174293e+01],\n",
              "       [ 9.94891137e+02, -1.00945460e+02],\n",
              "       [-6.69323504e+01, -2.35772380e+02],\n",
              "       [ 1.64911776e+03,  6.96619509e+01],\n",
              "       [-1.16401812e+03,  9.45451967e+01],\n",
              "       [ 2.03027733e+03,  8.83782540e+02],\n",
              "       [-1.46429649e+03,  1.20513325e+02],\n",
              "       [-1.39276874e+03,  1.04366287e+02],\n",
              "       [ 1.31091327e+03,  1.61691159e+02],\n",
              "       [ 8.47326760e+02,  9.46236652e-01],\n",
              "       [-1.65079380e+02, -2.47823562e+02],\n",
              "       [ 3.93517153e+02, -8.60629571e+01],\n",
              "       [-1.41580929e+03,  1.10895834e+02],\n",
              "       [-1.49213257e+03,  1.23897287e+02],\n",
              "       [ 2.44847837e+03,  2.31535803e+02],\n",
              "       [ 3.67341386e+02, -1.09345986e+02],\n",
              "       [-1.41332333e+03,  1.12535400e+02],\n",
              "       [-1.21958773e+03,  9.16158417e+01],\n",
              "       [-8.82190279e+02, -1.01399329e+01],\n",
              "       [ 1.79140832e+03,  5.22453592e+02],\n",
              "       [-3.49560853e+02,  5.58141277e+01],\n",
              "       [ 5.38362800e+02,  5.20482014e+01],\n",
              "       [-1.31899761e+03,  1.08767911e+02],\n",
              "       [-9.31742102e+02,  3.40522677e+01],\n",
              "       [-1.07326045e+03,  4.45348430e+01],\n",
              "       [-1.68245718e+02, -2.91854957e+02],\n",
              "       [ 1.28006471e+03,  4.63953845e+01],\n",
              "       [ 2.64038062e+03,  5.00551736e+02],\n",
              "       [ 7.92068393e+02, -1.85614244e+02],\n",
              "       [-5.17083189e+02,  5.20377498e+01],\n",
              "       [-7.64435722e+02, -6.49487296e+01],\n",
              "       [-3.97506665e+02, -1.45158862e+02],\n",
              "       [ 2.02120923e+03,  1.80863000e+02],\n",
              "       [ 1.78502866e+03,  5.97143289e+01],\n",
              "       [ 3.44836328e+02, -3.87281552e+02],\n",
              "       [ 1.45655549e+03,  3.89198094e+02],\n",
              "       [ 1.43616381e+03, -4.20322649e+02],\n",
              "       [ 3.30767832e+03,  9.85269983e+02],\n",
              "       [-1.49282600e+03,  1.25108990e+02],\n",
              "       [-1.05764898e+03,  3.85969855e+01],\n",
              "       [ 2.45048526e+03,  2.11984256e+02],\n",
              "       [-1.47754866e+03,  1.20680675e+02],\n",
              "       [-9.74495370e+01, -3.25539442e+01],\n",
              "       [-5.25216154e+02, -8.24616606e+01],\n",
              "       [ 1.21744663e+03,  2.74240331e+02],\n",
              "       [ 1.39078277e+03,  2.91469897e+02],\n",
              "       [-3.73990057e+02,  8.52892635e+00],\n",
              "       [-9.33664851e+02,  4.59745435e+01],\n",
              "       [ 1.29420061e+03,  1.34040673e+02],\n",
              "       [-1.45720487e+03,  1.25616694e+02],\n",
              "       [ 7.64578799e+02, -3.26160932e+02],\n",
              "       [-1.16724433e+03,  3.99156762e+01],\n",
              "       [ 1.32813153e+01, -3.39509016e+02],\n",
              "       [ 3.04573250e+02, -9.89893067e+01],\n",
              "       [ 1.43332608e+03, -3.61165533e+02],\n",
              "       [-1.33446936e+03,  8.05939898e+01],\n",
              "       [-8.37271057e+02,  9.88016258e+01],\n",
              "       [-7.10130091e+02, -3.86635772e+01],\n",
              "       [ 1.20218716e+03, -3.58082980e+02],\n",
              "       [-3.48241495e+02, -2.29231414e+02],\n",
              "       [-1.68484409e+02,  1.74787451e+01],\n",
              "       [-4.04576718e+02, -2.00685152e+02],\n",
              "       [ 9.93134629e+02,  1.25469751e+02],\n",
              "       [ 1.15115831e+03,  4.43888088e+02],\n",
              "       [ 1.96619517e+03,  3.94602808e+02],\n",
              "       [-4.19003759e+02, -1.50332259e+02],\n",
              "       [ 2.46217823e+03,  2.73611549e+02],\n",
              "       [ 1.51871146e+03,  2.00578425e+02],\n",
              "       [ 5.05988515e+02,  5.49402292e+01],\n",
              "       [-1.03140388e+03,  2.80800471e+01],\n",
              "       [-1.47049574e+03,  1.17246889e+02],\n",
              "       [-9.99780132e+02,  1.22236660e+02],\n",
              "       [-8.78834890e+02,  5.65115919e+01],\n",
              "       [-1.17438638e+03,  9.09562525e+01],\n",
              "       [ 1.06266445e+03, -1.16331070e+01],\n",
              "       [ 1.77431413e+03,  1.13558055e+01],\n",
              "       [-4.95495004e+02, -1.42613544e+00],\n",
              "       [-1.13201004e+03,  2.29536607e+01],\n",
              "       [ 3.64505283e+02, -2.99230769e+02],\n",
              "       [ 4.91848242e+02, -3.21598192e+02],\n",
              "       [ 1.49814406e+03, -2.95941026e+01],\n",
              "       [ 2.06592299e+03,  1.37736082e+01],\n",
              "       [-5.41593300e+02, -9.31819228e+01],\n",
              "       [ 2.70921163e+03,  8.44890261e+02],\n",
              "       [-1.24668903e+03,  5.83037563e+01],\n",
              "       [-2.91144903e+02, -6.02450427e+01],\n",
              "       [-1.42494673e+03,  1.11574709e+02],\n",
              "       [-3.97776554e+02, -3.48083209e+01],\n",
              "       [ 7.11081683e+02, -2.01405524e+02],\n",
              "       [ 2.32346284e+03,  3.14795529e+02],\n",
              "       [ 6.22939605e+02, -3.51611945e+02],\n",
              "       [-1.15396362e+03,  4.64481818e+01],\n",
              "       [-8.75647089e+02,  5.83416531e+01],\n",
              "       [-5.45728515e+02, -1.80336481e+01],\n",
              "       [-1.49597348e+03,  1.22789525e+02],\n",
              "       [-1.28455340e+03,  1.11122065e+02],\n",
              "       [-1.24331855e+03,  1.00123768e+02],\n",
              "       [ 8.80998366e+02,  1.38668326e+02],\n",
              "       [-7.82285256e+02, -6.30527629e+00],\n",
              "       [ 1.55685218e+03, -1.43272147e+02],\n",
              "       [-7.26643694e+02, -1.03778591e+02],\n",
              "       [ 1.97248190e+03, -7.24659040e+01],\n",
              "       [ 1.56124101e+03,  4.83256752e+01],\n",
              "       [-3.60676024e+02,  1.01350594e+02],\n",
              "       [ 8.29048200e+02,  9.83279762e+01],\n",
              "       [ 1.38105976e+03, -6.85224797e+01],\n",
              "       [ 8.93680412e+02,  1.68470265e+02],\n",
              "       [ 5.29097483e+02,  2.03651659e+02],\n",
              "       [ 1.16455584e+03, -4.98544909e+02],\n",
              "       [ 4.45165792e+02,  1.74490579e+02],\n",
              "       [-1.27731690e+03,  4.38604927e+01],\n",
              "       [ 8.48866962e+02, -3.93407354e+02],\n",
              "       [-7.04947978e+01,  5.73335857e+01],\n",
              "       [-9.89765643e+02,  7.32065189e+00],\n",
              "       [-1.49722783e+03,  1.26916286e+02],\n",
              "       [-1.85079185e+02,  2.53505024e+02],\n",
              "       [ 1.73136256e+03,  3.66999284e+02],\n",
              "       [-7.94282648e+02, -6.27534570e+01],\n",
              "       [ 2.25161465e+02,  1.03921476e+02],\n",
              "       [ 1.90719689e+03,  1.41003622e+02],\n",
              "       [ 6.93198118e+02, -4.38223517e+02],\n",
              "       [ 3.44087662e+02,  4.30152422e+01],\n",
              "       [ 1.92023078e+03,  3.06230147e+02],\n",
              "       [-1.50018078e+03,  1.26415944e+02],\n",
              "       [ 1.06775465e+03, -2.97944001e+02],\n",
              "       [ 1.36754712e+03, -2.61472180e+00],\n",
              "       [-1.39923927e+03,  8.67351368e+01],\n",
              "       [-1.35337942e+03,  8.37628610e+01],\n",
              "       [-1.45560095e+03,  1.10389458e+02],\n",
              "       [ 2.71220836e+03,  6.58556817e+02],\n",
              "       [-1.42547227e+03,  1.02410116e+02],\n",
              "       [-1.21131751e+03,  8.54183544e+01],\n",
              "       [-1.45871552e+03,  1.11735179e+02],\n",
              "       [ 2.27456826e+03,  7.92395864e+02],\n",
              "       [ 1.00471161e+03, -3.58078079e+02],\n",
              "       [-1.33604635e+03,  8.23464065e+01],\n",
              "       [ 1.90588474e+03, -1.44567204e+02],\n",
              "       [-6.45135835e+02, -1.15120079e+01],\n",
              "       [ 1.09003731e+03, -1.77140482e+02],\n",
              "       [-6.48488475e+02, -1.00410260e+02],\n",
              "       [-3.57834581e+02,  4.78978382e+00],\n",
              "       [ 1.69161371e+03, -1.14981855e+02],\n",
              "       [-1.08025125e+03,  2.21765095e+01],\n",
              "       [ 9.97473988e+02, -4.16094467e+02],\n",
              "       [-1.48931117e+03,  1.23438499e+02],\n",
              "       [-1.24258346e+03,  5.90180291e+01],\n",
              "       [-6.83768341e+02, -7.73549582e+01],\n",
              "       [-1.41856329e+03,  1.02218438e+02],\n",
              "       [ 1.09426926e+03,  4.39362344e+01],\n",
              "       [ 1.73966912e+03, -3.97054635e+02],\n",
              "       [-4.21950779e+02, -1.41841253e+01],\n",
              "       [-4.68126410e+02,  2.45616552e+02],\n",
              "       [ 1.29207369e+03,  2.36995917e+02],\n",
              "       [-1.34364493e+03,  7.53256928e+01],\n",
              "       [-1.02901022e+00, -1.32918949e+02],\n",
              "       [ 1.77347024e+02, -1.40593370e+02],\n",
              "       [-5.87364213e+02, -7.72444196e+01],\n",
              "       [ 1.66359493e+03,  1.51905923e+02],\n",
              "       [ 1.12773163e+03, -5.80662487e+02],\n",
              "       [ 1.67657400e+02,  6.87308991e+01],\n",
              "       [-1.43608217e+03,  1.20717598e+02],\n",
              "       [-1.41235050e+03,  9.76735928e+01],\n",
              "       [-7.83375390e+02, -1.04012718e+02],\n",
              "       [ 3.70713568e+02, -7.35711099e+01],\n",
              "       [-1.23676597e+03,  9.38516265e+01],\n",
              "       [-9.71136403e+02,  2.42006663e+00],\n",
              "       [ 1.30586126e+03,  2.89353025e+01],\n",
              "       [-1.46133359e+03,  1.27606236e+02],\n",
              "       [ 7.72802389e+02, -2.14050744e+02],\n",
              "       [ 1.64363275e+03,  4.35823187e+02],\n",
              "       [-1.10976857e+03,  4.51219867e+01],\n",
              "       [ 8.53361690e+02, -6.26292363e+01],\n",
              "       [-1.09875241e+03,  6.18857950e+01],\n",
              "       [-6.85651000e+02,  1.09942514e+02],\n",
              "       [-4.80176315e+02, -1.87000774e+02],\n",
              "       [-1.50273574e+03,  1.29951112e+02],\n",
              "       [ 2.37121533e+03,  7.82547896e+01],\n",
              "       [ 9.51510128e+02, -1.48876787e+02],\n",
              "       [ 7.12523498e+02, -2.22338158e+02],\n",
              "       [-7.50796742e+02,  2.37220796e+02],\n",
              "       [ 1.50921295e+03, -2.13725479e+02],\n",
              "       [ 2.65604567e+03,  6.39102055e+02],\n",
              "       [ 2.09058129e+03, -1.70321656e+02],\n",
              "       [-8.65059145e+02, -3.75294173e+01],\n",
              "       [ 2.32683631e+02, -1.52884190e+02],\n",
              "       [-2.16937175e+02, -2.36971325e+02],\n",
              "       [ 1.25165485e+03, -3.13003659e+02],\n",
              "       [ 2.01185119e+02, -3.36600019e+02],\n",
              "       [-5.17006690e+02, -7.80963184e+01],\n",
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              "       [ 2.15517336e+02, -1.01909301e+02],\n",
              "       [ 4.29722304e+02, -2.16380171e+02],\n",
              "       [ 6.82127690e+02, -1.58081629e+02],\n",
              "       [-1.48191169e+03,  1.29495605e+02],\n",
              "       [-6.96843559e+02, -1.17666768e+02],\n",
              "       [-7.63064463e+02, -2.64872807e+01],\n",
              "       [-1.72487606e+01, -1.06890345e+02],\n",
              "       [-1.04945098e+03,  5.90383273e+01],\n",
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              "       [-1.35704388e+03,  1.14583043e+02],\n",
              "       [-3.93056893e+02, -1.34370201e+02],\n",
              "       [ 8.58413650e+02,  2.22944867e+02],\n",
              "       [ 9.64029694e+02,  2.20657369e+02],\n",
              "       [ 2.12196118e+03,  2.37915366e+02],\n",
              "       [ 1.73920841e+02, -1.71342222e+01],\n",
              "       [-2.47464059e+02,  3.48528512e+01]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "98dplDNnvPQ-",
        "colab_type": "code",
        "outputId": "0392a223-a000-4812-8876-34e4ce0cef1c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 151
        }
      },
      "source": [
        "#Find the 'good columns' data for the player LeBron James\n",
        "#again by 'good columns' I mean with only numeric values, and no missing values (NA, Nan, etc).\n",
        "\n",
        "# Find player LeBron\n",
        "LeBron = good_columns.loc[ nba['player'] == 'LeBron James',: ]\n",
        "\n",
        "#Find player Durant\n",
        "Durant = good_columns.loc[ nba['player'] == 'Kevin Durant',: ]\n",
        "\n",
        "#print the players\n",
        "print(LeBron)\n",
        "print(Durant)"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "     age   g  gs    mp   fg   fga  ...  stl  blk  tov   pf   pts  season_end\n",
            "225   29  77  77  2902  767  1353  ...  121   26  270  126  2089        2013\n",
            "\n",
            "[1 rows x 22 columns]\n",
            "     age   g  gs    mp   fg   fga  ...  stl  blk  tov   pf   pts  season_end\n",
            "133   25  81  81  3122  849  1688  ...  103   59  285  174  2593        2013\n",
            "\n",
            "[1 rows x 22 columns]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mSFzCjZAyRYT",
        "colab_type": "code",
        "outputId": "5c669ad7-9adb-43f7-8104-4fcb8e7263ef",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "source": [
        "#Change the dataframe to a list Lebron to be able to use the kmeans model to make predictions/grouping\n",
        "Lebron_list = LeBron.values.tolist()\n",
        "Durant_list = Durant.values.tolist()\n",
        "\n",
        "#Predict which group LeBron James and Kevin Durant belongs\n",
        "LeBron_Cluster_Label = kmeans_model.predict(Lebron_list)\n",
        "Durant_Cluster_Label = kmeans_model.predict(Durant_list)\n",
        "\n",
        "print(LeBron_Cluster_Label)\n",
        "print(Durant_Cluster_Label)"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[3]\n",
            "[3]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DOnqs6QT60Wu",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 866
        },
        "outputId": "40b73e08-1e22-4d12-8463-ee1cfd06a4bc"
      },
      "source": [
        "\n",
        "# Look at all of the column coorelations\n",
        "nba.corr() #Note there is a positive coorelation between minutes played(mp) and points(pts)"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>g</th>\n",
              "      <th>gs</th>\n",
              "      <th>mp</th>\n",
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              "      <th>blk</th>\n",
              "      <th>tov</th>\n",
              "      <th>pf</th>\n",
              "      <th>pts</th>\n",
              "      <th>season_end</th>\n",
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              "      <td>-0.030789</td>\n",
              "      <td>-0.028221</td>\n",
              "      <td>-0.011910</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
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              "      <td>-0.012074</td>\n",
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              "      <td>0.615001</td>\n",
              "      <td>0.252328</td>\n",
              "      <td>0.546902</td>\n",
              "      <td>0.707389</td>\n",
              "      <td>0.682688</td>\n",
              "      <td>0.551128</td>\n",
              "      <td>0.709650</td>\n",
              "      <td>0.475581</td>\n",
              "      <td>0.713508</td>\n",
              "      <td>0.865797</td>\n",
              "      <td>0.728462</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>gs</th>\n",
              "      <td>0.025163</td>\n",
              "      <td>0.610951</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.860036</td>\n",
              "      <td>0.821619</td>\n",
              "      <td>0.811531</td>\n",
              "      <td>0.234677</td>\n",
              "      <td>0.501808</td>\n",
              "      <td>0.515718</td>\n",
              "      <td>0.063468</td>\n",
              "      <td>0.785619</td>\n",
              "      <td>0.784812</td>\n",
              "      <td>0.205289</td>\n",
              "      <td>0.231222</td>\n",
              "      <td>0.707049</td>\n",
              "      <td>0.720527</td>\n",
              "      <td>0.178607</td>\n",
              "      <td>0.560067</td>\n",
              "      <td>0.774892</td>\n",
              "      <td>0.735738</td>\n",
              "      <td>0.636059</td>\n",
              "      <td>0.743178</td>\n",
              "      <td>0.505589</td>\n",
              "      <td>0.767107</td>\n",
              "      <td>0.725573</td>\n",
              "      <td>0.810294</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mp</th>\n",
              "      <td>0.007961</td>\n",
              "      <td>0.864487</td>\n",
              "      <td>0.860036</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.931120</td>\n",
              "      <td>0.936883</td>\n",
              "      <td>0.273682</td>\n",
              "      <td>0.645056</td>\n",
              "      <td>0.666126</td>\n",
              "      <td>0.138230</td>\n",
              "      <td>0.863941</td>\n",
              "      <td>0.874109</td>\n",
              "      <td>0.243727</td>\n",
              "      <td>0.304770</td>\n",
              "      <td>0.805468</td>\n",
              "      <td>0.814450</td>\n",
              "      <td>0.278872</td>\n",
              "      <td>0.576844</td>\n",
              "      <td>0.821145</td>\n",
              "      <td>0.774492</td>\n",
              "      <td>0.733041</td>\n",
              "      <td>0.852331</td>\n",
              "      <td>0.506254</td>\n",
              "      <td>0.885406</td>\n",
              "      <td>0.884484</td>\n",
              "      <td>0.927464</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>fg</th>\n",
              "      <td>-0.009749</td>\n",
              "      <td>0.739993</td>\n",
              "      <td>0.821619</td>\n",
              "      <td>0.931120</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.988262</td>\n",
              "      <td>0.278007</td>\n",
              "      <td>0.597239</td>\n",
              "      <td>0.613988</td>\n",
              "      <td>0.110514</td>\n",
              "      <td>0.960853</td>\n",
              "      <td>0.962059</td>\n",
              "      <td>0.238487</td>\n",
              "      <td>0.277694</td>\n",
              "      <td>0.893619</td>\n",
              "      <td>0.895138</td>\n",
              "      <td>0.277730</td>\n",
              "      <td>0.562293</td>\n",
              "      <td>0.820259</td>\n",
              "      <td>0.769339</td>\n",
              "      <td>0.708228</td>\n",
              "      <td>0.786597</td>\n",
              "      <td>0.484208</td>\n",
              "      <td>0.903383</td>\n",
              "      <td>0.798769</td>\n",
              "      <td>0.992041</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>fga</th>\n",
              "      <td>-0.018304</td>\n",
              "      <td>0.746963</td>\n",
              "      <td>0.811531</td>\n",
              "      <td>0.936883</td>\n",
              "      <td>0.988262</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.211174</td>\n",
              "      <td>0.662004</td>\n",
              "      <td>0.685535</td>\n",
              "      <td>0.152111</td>\n",
              "      <td>0.924781</td>\n",
              "      <td>0.944490</td>\n",
              "      <td>0.185373</td>\n",
              "      <td>0.236838</td>\n",
              "      <td>0.887922</td>\n",
              "      <td>0.877945</td>\n",
              "      <td>0.312489</td>\n",
              "      <td>0.487154</td>\n",
              "      <td>0.771821</td>\n",
              "      <td>0.710910</td>\n",
              "      <td>0.748141</td>\n",
              "      <td>0.803290</td>\n",
              "      <td>0.412738</td>\n",
              "      <td>0.910689</td>\n",
              "      <td>0.786560</td>\n",
              "      <td>0.989211</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>fg.</th>\n",
              "      <td>0.025221</td>\n",
              "      <td>0.322201</td>\n",
              "      <td>0.234677</td>\n",
              "      <td>0.273682</td>\n",
              "      <td>0.278007</td>\n",
              "      <td>0.211174</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.025510</td>\n",
              "      <td>-0.041720</td>\n",
              "      <td>-0.039424</td>\n",
              "      <td>0.333179</td>\n",
              "      <td>0.283523</td>\n",
              "      <td>0.880201</td>\n",
              "      <td>0.908930</td>\n",
              "      <td>0.217450</td>\n",
              "      <td>0.258550</td>\n",
              "      <td>-0.008697</td>\n",
              "      <td>0.423358</td>\n",
              "      <td>0.377067</td>\n",
              "      <td>0.404832</td>\n",
              "      <td>0.068105</td>\n",
              "      <td>0.185385</td>\n",
              "      <td>0.401431</td>\n",
              "      <td>0.221846</td>\n",
              "      <td>0.359125</td>\n",
              "      <td>0.248276</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>x3p</th>\n",
              "      <td>0.050611</td>\n",
              "      <td>0.518074</td>\n",
              "      <td>0.501808</td>\n",
              "      <td>0.645056</td>\n",
              "      <td>0.597239</td>\n",
              "      <td>0.662004</td>\n",
              "      <td>-0.025510</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.991700</td>\n",
              "      <td>0.462709</td>\n",
              "      <td>0.351640</td>\n",
              "      <td>0.382531</td>\n",
              "      <td>0.041768</td>\n",
              "      <td>0.219614</td>\n",
              "      <td>0.503353</td>\n",
              "      <td>0.441246</td>\n",
              "      <td>0.369515</td>\n",
              "      <td>-0.065822</td>\n",
              "      <td>0.280171</td>\n",
              "      <td>0.182848</td>\n",
              "      <td>0.617553</td>\n",
              "      <td>0.592092</td>\n",
              "      <td>-0.043707</td>\n",
              "      <td>0.560520</td>\n",
              "      <td>0.446711</td>\n",
              "      <td>0.655342</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>x3pa</th>\n",
              "      <td>0.028850</td>\n",
              "      <td>0.537011</td>\n",
              "      <td>0.515718</td>\n",
              "      <td>0.666126</td>\n",
              "      <td>0.613988</td>\n",
              "      <td>0.685535</td>\n",
              "      <td>-0.041720</td>\n",
              "      <td>0.991700</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.449886</td>\n",
              "      <td>0.374057</td>\n",
              "      <td>0.408290</td>\n",
              "      <td>0.042401</td>\n",
              "      <td>0.196420</td>\n",
              "      <td>0.527835</td>\n",
              "      <td>0.467615</td>\n",
              "      <td>0.370768</td>\n",
              "      <td>-0.058075</td>\n",
              "      <td>0.291838</td>\n",
              "      <td>0.193712</td>\n",
              "      <td>0.643211</td>\n",
              "      <td>0.622973</td>\n",
              "      <td>-0.040987</td>\n",
              "      <td>0.589799</td>\n",
              "      <td>0.463455</td>\n",
              "      <td>0.672076</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>x3p.</th>\n",
              "      <td>0.014235</td>\n",
              "      <td>0.103762</td>\n",
              "      <td>0.063468</td>\n",
              "      <td>0.138230</td>\n",
              "      <td>0.110514</td>\n",
              "      <td>0.152111</td>\n",
              "      <td>-0.039424</td>\n",
              "      <td>0.462709</td>\n",
              "      <td>0.449886</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.032484</td>\n",
              "      <td>-0.013775</td>\n",
              "      <td>-0.118689</td>\n",
              "      <td>0.296875</td>\n",
              "      <td>0.059541</td>\n",
              "      <td>0.017214</td>\n",
              "      <td>0.289435</td>\n",
              "      <td>-0.314490</td>\n",
              "      <td>-0.094895</td>\n",
              "      <td>-0.166848</td>\n",
              "      <td>0.212819</td>\n",
              "      <td>0.150476</td>\n",
              "      <td>-0.187411</td>\n",
              "      <td>0.097876</td>\n",
              "      <td>-0.019089</td>\n",
              "      <td>0.144431</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>x2p</th>\n",
              "      <td>-0.028862</td>\n",
              "      <td>0.684729</td>\n",
              "      <td>0.785619</td>\n",
              "      <td>0.863941</td>\n",
              "      <td>0.960853</td>\n",
              "      <td>0.924781</td>\n",
              "      <td>0.333179</td>\n",
              "      <td>0.351640</td>\n",
              "      <td>0.374057</td>\n",
              "      <td>-0.032484</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.990736</td>\n",
              "      <td>0.263757</td>\n",
              "      <td>0.248055</td>\n",
              "      <td>0.869121</td>\n",
              "      <td>0.892348</td>\n",
              "      <td>0.193893</td>\n",
              "      <td>0.679022</td>\n",
              "      <td>0.860592</td>\n",
              "      <td>0.834779</td>\n",
              "      <td>0.613292</td>\n",
              "      <td>0.713556</td>\n",
              "      <td>0.580246</td>\n",
              "      <td>0.860769</td>\n",
              "      <td>0.777982</td>\n",
              "      <td>0.931493</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>x2pa</th>\n",
              "      <td>-0.035970</td>\n",
              "      <td>0.694243</td>\n",
              "      <td>0.784812</td>\n",
              "      <td>0.874109</td>\n",
              "      <td>0.962059</td>\n",
              "      <td>0.944490</td>\n",
              "      <td>0.283523</td>\n",
              "      <td>0.382531</td>\n",
              "      <td>0.408290</td>\n",
              "      <td>-0.013775</td>\n",
              "      <td>0.990736</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.213142</td>\n",
              "      <td>0.208123</td>\n",
              "      <td>0.875126</td>\n",
              "      <td>0.889792</td>\n",
              "      <td>0.221246</td>\n",
              "      <td>0.637025</td>\n",
              "      <td>0.836051</td>\n",
              "      <td>0.803959</td>\n",
              "      <td>0.647796</td>\n",
              "      <td>0.726077</td>\n",
              "      <td>0.536007</td>\n",
              "      <td>0.875709</td>\n",
              "      <td>0.777085</td>\n",
              "      <td>0.937036</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>x2p.</th>\n",
              "      <td>0.011306</td>\n",
              "      <td>0.283050</td>\n",
              "      <td>0.205289</td>\n",
              "      <td>0.243727</td>\n",
              "      <td>0.238487</td>\n",
              "      <td>0.185373</td>\n",
              "      <td>0.880201</td>\n",
              "      <td>0.041768</td>\n",
              "      <td>0.042401</td>\n",
              "      <td>-0.118689</td>\n",
              "      <td>0.263757</td>\n",
              "      <td>0.213142</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.828471</td>\n",
              "      <td>0.184123</td>\n",
              "      <td>0.213780</td>\n",
              "      <td>0.027333</td>\n",
              "      <td>0.313771</td>\n",
              "      <td>0.303140</td>\n",
              "      <td>0.317239</td>\n",
              "      <td>0.069485</td>\n",
              "      <td>0.185851</td>\n",
              "      <td>0.303162</td>\n",
              "      <td>0.188567</td>\n",
              "      <td>0.297251</td>\n",
              "      <td>0.219348</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>efg.</th>\n",
              "      <td>0.073002</td>\n",
              "      <td>0.351884</td>\n",
              "      <td>0.231222</td>\n",
              "      <td>0.304770</td>\n",
              "      <td>0.277694</td>\n",
              "      <td>0.236838</td>\n",
              "      <td>0.908930</td>\n",
              "      <td>0.219614</td>\n",
              "      <td>0.196420</td>\n",
              "      <td>0.296875</td>\n",
              "      <td>0.248055</td>\n",
              "      <td>0.208123</td>\n",
              "      <td>0.828471</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.191934</td>\n",
              "      <td>0.210592</td>\n",
              "      <td>0.166761</td>\n",
              "      <td>0.261287</td>\n",
              "      <td>0.299220</td>\n",
              "      <td>0.298092</td>\n",
              "      <td>0.130981</td>\n",
              "      <td>0.229058</td>\n",
              "      <td>0.267443</td>\n",
              "      <td>0.216401</td>\n",
              "      <td>0.329529</td>\n",
              "      <td>0.268952</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ft</th>\n",
              "      <td>-0.046554</td>\n",
              "      <td>0.598333</td>\n",
              "      <td>0.707049</td>\n",
              "      <td>0.805468</td>\n",
              "      <td>0.893619</td>\n",
              "      <td>0.887922</td>\n",
              "      <td>0.217450</td>\n",
              "      <td>0.503353</td>\n",
              "      <td>0.527835</td>\n",
              "      <td>0.059541</td>\n",
              "      <td>0.869121</td>\n",
              "      <td>0.875126</td>\n",
              "      <td>0.184123</td>\n",
              "      <td>0.191934</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.986227</td>\n",
              "      <td>0.271157</td>\n",
              "      <td>0.462211</td>\n",
              "      <td>0.704035</td>\n",
              "      <td>0.654005</td>\n",
              "      <td>0.699452</td>\n",
              "      <td>0.720571</td>\n",
              "      <td>0.383611</td>\n",
              "      <td>0.872003</td>\n",
              "      <td>0.663557</td>\n",
              "      <td>0.927618</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>fta</th>\n",
              "      <td>-0.061751</td>\n",
              "      <td>0.615001</td>\n",
              "      <td>0.720527</td>\n",
              "      <td>0.814450</td>\n",
              "      <td>0.895138</td>\n",
              "      <td>0.877945</td>\n",
              "      <td>0.258550</td>\n",
              "      <td>0.441246</td>\n",
              "      <td>0.467615</td>\n",
              "      <td>0.017214</td>\n",
              "      <td>0.892348</td>\n",
              "      <td>0.889792</td>\n",
              "      <td>0.213780</td>\n",
              "      <td>0.210592</td>\n",
              "      <td>0.986227</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.201001</td>\n",
              "      <td>0.544659</td>\n",
              "      <td>0.757296</td>\n",
              "      <td>0.718198</td>\n",
              "      <td>0.670307</td>\n",
              "      <td>0.727189</td>\n",
              "      <td>0.456063</td>\n",
              "      <td>0.877715</td>\n",
              "      <td>0.698489</td>\n",
              "      <td>0.918979</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ft.</th>\n",
              "      <td>0.021481</td>\n",
              "      <td>0.252328</td>\n",
              "      <td>0.178607</td>\n",
              "      <td>0.278872</td>\n",
              "      <td>0.277730</td>\n",
              "      <td>0.312489</td>\n",
              "      <td>-0.008697</td>\n",
              "      <td>0.369515</td>\n",
              "      <td>0.370768</td>\n",
              "      <td>0.289435</td>\n",
              "      <td>0.193893</td>\n",
              "      <td>0.221246</td>\n",
              "      <td>0.027333</td>\n",
              "      <td>0.166761</td>\n",
              "      <td>0.271157</td>\n",
              "      <td>0.201001</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.049071</td>\n",
              "      <td>0.093540</td>\n",
              "      <td>0.052432</td>\n",
              "      <td>0.278527</td>\n",
              "      <td>0.221823</td>\n",
              "      <td>-0.068472</td>\n",
              "      <td>0.248213</td>\n",
              "      <td>0.184079</td>\n",
              "      <td>0.303459</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>orb</th>\n",
              "      <td>-0.068726</td>\n",
              "      <td>0.546902</td>\n",
              "      <td>0.560067</td>\n",
              "      <td>0.576844</td>\n",
              "      <td>0.562293</td>\n",
              "      <td>0.487154</td>\n",
              "      <td>0.423358</td>\n",
              "      <td>-0.065822</td>\n",
              "      <td>-0.058075</td>\n",
              "      <td>-0.314490</td>\n",
              "      <td>0.679022</td>\n",
              "      <td>0.637025</td>\n",
              "      <td>0.313771</td>\n",
              "      <td>0.261287</td>\n",
              "      <td>0.462211</td>\n",
              "      <td>0.544659</td>\n",
              "      <td>-0.049071</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.839774</td>\n",
              "      <td>0.919166</td>\n",
              "      <td>0.142708</td>\n",
              "      <td>0.386100</td>\n",
              "      <td>0.782384</td>\n",
              "      <td>0.468581</td>\n",
              "      <td>0.713271</td>\n",
              "      <td>0.505524</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>drb</th>\n",
              "      <td>0.010822</td>\n",
              "      <td>0.707389</td>\n",
              "      <td>0.774892</td>\n",
              "      <td>0.821145</td>\n",
              "      <td>0.820259</td>\n",
              "      <td>0.771821</td>\n",
              "      <td>0.377067</td>\n",
              "      <td>0.280171</td>\n",
              "      <td>0.291838</td>\n",
              "      <td>-0.094895</td>\n",
              "      <td>0.860592</td>\n",
              "      <td>0.836051</td>\n",
              "      <td>0.303140</td>\n",
              "      <td>0.299220</td>\n",
              "      <td>0.704035</td>\n",
              "      <td>0.757296</td>\n",
              "      <td>0.093540</td>\n",
              "      <td>0.839774</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.985738</td>\n",
              "      <td>0.448866</td>\n",
              "      <td>0.631805</td>\n",
              "      <td>0.740026</td>\n",
              "      <td>0.736415</td>\n",
              "      <td>0.821327</td>\n",
              "      <td>0.784675</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>trb</th>\n",
              "      <td>-0.013451</td>\n",
              "      <td>0.682688</td>\n",
              "      <td>0.735738</td>\n",
              "      <td>0.774492</td>\n",
              "      <td>0.769339</td>\n",
              "      <td>0.710910</td>\n",
              "      <td>0.404832</td>\n",
              "      <td>0.182848</td>\n",
              "      <td>0.193712</td>\n",
              "      <td>-0.166848</td>\n",
              "      <td>0.834779</td>\n",
              "      <td>0.803959</td>\n",
              "      <td>0.317239</td>\n",
              "      <td>0.298092</td>\n",
              "      <td>0.654005</td>\n",
              "      <td>0.718198</td>\n",
              "      <td>0.052432</td>\n",
              "      <td>0.919166</td>\n",
              "      <td>0.985738</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.369862</td>\n",
              "      <td>0.578014</td>\n",
              "      <td>0.779353</td>\n",
              "      <td>0.679468</td>\n",
              "      <td>0.816910</td>\n",
              "      <td>0.725930</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ast</th>\n",
              "      <td>0.019216</td>\n",
              "      <td>0.551128</td>\n",
              "      <td>0.636059</td>\n",
              "      <td>0.733041</td>\n",
              "      <td>0.708228</td>\n",
              "      <td>0.748141</td>\n",
              "      <td>0.068105</td>\n",
              "      <td>0.617553</td>\n",
              "      <td>0.643211</td>\n",
              "      <td>0.212819</td>\n",
              "      <td>0.613292</td>\n",
              "      <td>0.647796</td>\n",
              "      <td>0.069485</td>\n",
              "      <td>0.130981</td>\n",
              "      <td>0.699452</td>\n",
              "      <td>0.670307</td>\n",
              "      <td>0.278527</td>\n",
              "      <td>0.142708</td>\n",
              "      <td>0.448866</td>\n",
              "      <td>0.369862</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.770428</td>\n",
              "      <td>0.104589</td>\n",
              "      <td>0.855144</td>\n",
              "      <td>0.538109</td>\n",
              "      <td>0.738295</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>stl</th>\n",
              "      <td>-0.028315</td>\n",
              "      <td>0.709650</td>\n",
              "      <td>0.743178</td>\n",
              "      <td>0.852331</td>\n",
              "      <td>0.786597</td>\n",
              "      <td>0.803290</td>\n",
              "      <td>0.185385</td>\n",
              "      <td>0.592092</td>\n",
              "      <td>0.622973</td>\n",
              "      <td>0.150476</td>\n",
              "      <td>0.713556</td>\n",
              "      <td>0.726077</td>\n",
              "      <td>0.185851</td>\n",
              "      <td>0.229058</td>\n",
              "      <td>0.720571</td>\n",
              "      <td>0.727189</td>\n",
              "      <td>0.221823</td>\n",
              "      <td>0.386100</td>\n",
              "      <td>0.631805</td>\n",
              "      <td>0.578014</td>\n",
              "      <td>0.770428</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.317737</td>\n",
              "      <td>0.826865</td>\n",
              "      <td>0.737628</td>\n",
              "      <td>0.797449</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>blk</th>\n",
              "      <td>-0.017398</td>\n",
              "      <td>0.475581</td>\n",
              "      <td>0.505589</td>\n",
              "      <td>0.506254</td>\n",
              "      <td>0.484208</td>\n",
              "      <td>0.412738</td>\n",
              "      <td>0.401431</td>\n",
              "      <td>-0.043707</td>\n",
              "      <td>-0.040987</td>\n",
              "      <td>-0.187411</td>\n",
              "      <td>0.580246</td>\n",
              "      <td>0.536007</td>\n",
              "      <td>0.303162</td>\n",
              "      <td>0.267443</td>\n",
              "      <td>0.383611</td>\n",
              "      <td>0.456063</td>\n",
              "      <td>-0.068472</td>\n",
              "      <td>0.782384</td>\n",
              "      <td>0.740026</td>\n",
              "      <td>0.779353</td>\n",
              "      <td>0.104589</td>\n",
              "      <td>0.317737</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.396247</td>\n",
              "      <td>0.633609</td>\n",
              "      <td>0.433549</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>tov</th>\n",
              "      <td>-0.030789</td>\n",
              "      <td>0.713508</td>\n",
              "      <td>0.767107</td>\n",
              "      <td>0.885406</td>\n",
              "      <td>0.903383</td>\n",
              "      <td>0.910689</td>\n",
              "      <td>0.221846</td>\n",
              "      <td>0.560520</td>\n",
              "      <td>0.589799</td>\n",
              "      <td>0.097876</td>\n",
              "      <td>0.860769</td>\n",
              "      <td>0.875709</td>\n",
              "      <td>0.188567</td>\n",
              "      <td>0.216401</td>\n",
              "      <td>0.872003</td>\n",
              "      <td>0.877715</td>\n",
              "      <td>0.248213</td>\n",
              "      <td>0.468581</td>\n",
              "      <td>0.736415</td>\n",
              "      <td>0.679468</td>\n",
              "      <td>0.855144</td>\n",
              "      <td>0.826865</td>\n",
              "      <td>0.396247</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.775430</td>\n",
              "      <td>0.912724</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>pf</th>\n",
              "      <td>-0.028221</td>\n",
              "      <td>0.865797</td>\n",
              "      <td>0.725573</td>\n",
              "      <td>0.884484</td>\n",
              "      <td>0.798769</td>\n",
              "      <td>0.786560</td>\n",
              "      <td>0.359125</td>\n",
              "      <td>0.446711</td>\n",
              "      <td>0.463455</td>\n",
              "      <td>-0.019089</td>\n",
              "      <td>0.777982</td>\n",
              "      <td>0.777085</td>\n",
              "      <td>0.297251</td>\n",
              "      <td>0.329529</td>\n",
              "      <td>0.663557</td>\n",
              "      <td>0.698489</td>\n",
              "      <td>0.184079</td>\n",
              "      <td>0.713271</td>\n",
              "      <td>0.821327</td>\n",
              "      <td>0.816910</td>\n",
              "      <td>0.538109</td>\n",
              "      <td>0.737628</td>\n",
              "      <td>0.633609</td>\n",
              "      <td>0.775430</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.778060</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>pts</th>\n",
              "      <td>-0.011910</td>\n",
              "      <td>0.728462</td>\n",
              "      <td>0.810294</td>\n",
              "      <td>0.927464</td>\n",
              "      <td>0.992041</td>\n",
              "      <td>0.989211</td>\n",
              "      <td>0.248276</td>\n",
              "      <td>0.655342</td>\n",
              "      <td>0.672076</td>\n",
              "      <td>0.144431</td>\n",
              "      <td>0.931493</td>\n",
              "      <td>0.937036</td>\n",
              "      <td>0.219348</td>\n",
              "      <td>0.268952</td>\n",
              "      <td>0.927618</td>\n",
              "      <td>0.918979</td>\n",
              "      <td>0.303459</td>\n",
              "      <td>0.505524</td>\n",
              "      <td>0.784675</td>\n",
              "      <td>0.725930</td>\n",
              "      <td>0.738295</td>\n",
              "      <td>0.797449</td>\n",
              "      <td>0.433549</td>\n",
              "      <td>0.912724</td>\n",
              "      <td>0.778060</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>season_end</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                 age         g        gs  ...        pf       pts  season_end\n",
              "age         1.000000 -0.012074  0.025163  ... -0.028221 -0.011910         NaN\n",
              "g          -0.012074  1.000000  0.610951  ...  0.865797  0.728462         NaN\n",
              "gs          0.025163  0.610951  1.000000  ...  0.725573  0.810294         NaN\n",
              "mp          0.007961  0.864487  0.860036  ...  0.884484  0.927464         NaN\n",
              "fg         -0.009749  0.739993  0.821619  ...  0.798769  0.992041         NaN\n",
              "fga        -0.018304  0.746963  0.811531  ...  0.786560  0.989211         NaN\n",
              "fg.         0.025221  0.322201  0.234677  ...  0.359125  0.248276         NaN\n",
              "x3p         0.050611  0.518074  0.501808  ...  0.446711  0.655342         NaN\n",
              "x3pa        0.028850  0.537011  0.515718  ...  0.463455  0.672076         NaN\n",
              "x3p.        0.014235  0.103762  0.063468  ... -0.019089  0.144431         NaN\n",
              "x2p        -0.028862  0.684729  0.785619  ...  0.777982  0.931493         NaN\n",
              "x2pa       -0.035970  0.694243  0.784812  ...  0.777085  0.937036         NaN\n",
              "x2p.        0.011306  0.283050  0.205289  ...  0.297251  0.219348         NaN\n",
              "efg.        0.073002  0.351884  0.231222  ...  0.329529  0.268952         NaN\n",
              "ft         -0.046554  0.598333  0.707049  ...  0.663557  0.927618         NaN\n",
              "fta        -0.061751  0.615001  0.720527  ...  0.698489  0.918979         NaN\n",
              "ft.         0.021481  0.252328  0.178607  ...  0.184079  0.303459         NaN\n",
              "orb        -0.068726  0.546902  0.560067  ...  0.713271  0.505524         NaN\n",
              "drb         0.010822  0.707389  0.774892  ...  0.821327  0.784675         NaN\n",
              "trb        -0.013451  0.682688  0.735738  ...  0.816910  0.725930         NaN\n",
              "ast         0.019216  0.551128  0.636059  ...  0.538109  0.738295         NaN\n",
              "stl        -0.028315  0.709650  0.743178  ...  0.737628  0.797449         NaN\n",
              "blk        -0.017398  0.475581  0.505589  ...  0.633609  0.433549         NaN\n",
              "tov        -0.030789  0.713508  0.767107  ...  0.775430  0.912724         NaN\n",
              "pf         -0.028221  0.865797  0.725573  ...  1.000000  0.778060         NaN\n",
              "pts        -0.011910  0.728462  0.810294  ...  0.778060  1.000000         NaN\n",
              "season_end       NaN       NaN       NaN  ...       NaN       NaN         NaN\n",
              "\n",
              "[27 rows x 27 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-HFf5Qla8B-l",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Let’s say we want to predict number of assists per player from field goals made per player.\n",
        "#Split the data into 80% training and 20% testing\n",
        "from sklearn.model_selection import train_test_split\n",
        "x_train, x_test, y_train, y_test = train_test_split(nba[['fg']], nba[['ast']], test_size=0.2, random_state=42)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H5VWdyxO2pD-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "271b664b-767b-436d-bcc3-3c9aef80af34"
      },
      "source": [
        "#Create the Linear Regression Model\n",
        "from sklearn.linear_model import LinearRegression\n",
        "lr = LinearRegression() # Create the model\n",
        "lr.fit(x_train, y_train) #Train the model\n",
        "predictions = lr.predict(x_test) #Make predictions on the test data\n",
        "\n",
        "print(predictions)\n",
        "print(y_test)"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[121.94310565]\n",
            " [ 24.72153471]\n",
            " [ 77.60579583]\n",
            " [252.28411286]\n",
            " [219.69886106]\n",
            " [ 70.12721345]\n",
            " [ 17.77713678]\n",
            " [178.03247351]\n",
            " [ 58.37515542]\n",
            " [ 70.12721345]\n",
            " [117.13544555]\n",
            " [ 30.06337926]\n",
            " [214.3570165 ]\n",
            " [ 24.18735025]\n",
            " [143.84466834]\n",
            " [ 22.05061243]\n",
            " [133.69516368]\n",
            " [  9.23018549]\n",
            " [131.55842586]\n",
            " [167.88296885]\n",
            " [ 21.51642797]\n",
            " [ 45.02054402]\n",
            " [ 31.13174818]\n",
            " [102.17828079]\n",
            " [233.05347245]\n",
            " [ 67.45629117]\n",
            " [236.25857919]\n",
            " [ 38.0761461 ]\n",
            " [ 64.78536889]\n",
            " [ 43.41799066]\n",
            " [  8.69600103]\n",
            " [ 16.70876787]\n",
            " [119.80636783]\n",
            " [266.17290871]\n",
            " [ 24.18735025]\n",
            " [152.92580409]\n",
            " [151.85743518]\n",
            " [ 22.58479688]\n",
            " [ 38.0761461 ]\n",
            " [ 14.57203005]\n",
            " [ 16.17458341]\n",
            " [301.42908279]\n",
            " [  8.16181658]\n",
            " [160.93857092]\n",
            " [ 46.08891294]\n",
            " [  9.76436994]\n",
            " [  8.16181658]\n",
            " [ 16.17458341]\n",
            " [ 71.19558236]\n",
            " [  9.76436994]\n",
            " [ 48.75983522]\n",
            " [157.73346419]\n",
            " [ 19.37969015]\n",
            " [100.04154297]\n",
            " [ 10.2985544 ]\n",
            " [ 83.48182484]\n",
            " [ 24.72153471]\n",
            " [323.86482993]\n",
            " [  9.76436994]\n",
            " [ 26.32408807]\n",
            " [ 82.94764038]\n",
            " [144.3788528 ]\n",
            " [106.45175644]\n",
            " [  9.76436994]\n",
            " [196.72892946]\n",
            " [127.81913467]\n",
            " [ 24.72153471]\n",
            " [ 24.72153471]\n",
            " [211.15190977]\n",
            " [292.34794704]\n",
            " [112.32778545]\n",
            " [ 84.0160093 ]\n",
            " [116.6012611 ]\n",
            " [ 83.48182484]\n",
            " [187.64779371]\n",
            " [108.0543098 ]\n",
            " [ 92.02877613]\n",
            " [192.45545381]\n",
            " [107.52012535]\n",
            " [ 35.40522382]\n",
            " [ 11.36692331]\n",
            " [ 90.96040722]\n",
            " [ 49.29401967]\n",
            " [ 83.48182484]\n",
            " [ 73.86650464]\n",
            " [ 28.99501035]\n",
            " [  9.76436994]\n",
            " [ 47.15728185]\n",
            " [ 21.51642797]\n",
            " [135.29771705]\n",
            " [ 47.15728185]\n",
            " [ 11.36692331]\n",
            " [ 27.39245698]\n",
            " [ 45.55472848]\n",
            " [ 35.93940828]\n",
            " [282.19844238]\n",
            " [186.04524035]]\n",
            "     ast\n",
            "73    82\n",
            "415   36\n",
            "392   52\n",
            "278  586\n",
            "400  219\n",
            "172   41\n",
            "395   34\n",
            "376   79\n",
            "77    29\n",
            "452  143\n",
            "9     94\n",
            "268   18\n",
            "244   87\n",
            "299   63\n",
            "30   303\n",
            "93    15\n",
            "55   112\n",
            "424    3\n",
            "33   125\n",
            "203  153\n",
            "180   30\n",
            "470   26\n",
            "70    12\n",
            "247   58\n",
            "104  223\n",
            "475   64\n",
            "408  359\n",
            "322   49\n",
            "302  197\n",
            "0     28\n",
            "..   ...\n",
            "68    38\n",
            "72   212\n",
            "218  433\n",
            "311  112\n",
            "79   207\n",
            "208  268\n",
            "148  117\n",
            "154   98\n",
            "175  152\n",
            "250   95\n",
            "476  217\n",
            "335   78\n",
            "75    17\n",
            "422    3\n",
            "15    17\n",
            "153   36\n",
            "373   34\n",
            "19    58\n",
            "312   44\n",
            "321    2\n",
            "56    31\n",
            "394    6\n",
            "211   76\n",
            "320   14\n",
            "222    5\n",
            "238   20\n",
            "409   36\n",
            "25    28\n",
            "265  147\n",
            "132  186\n",
            "\n",
            "[97 rows x 1 columns]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t7DuJTVY3W2G",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "outputId": "b69c5e34-02b1-4967-bd24-a5ad59becd28"
      },
      "source": [
        "# Testing Model: Score returns the coefficient of determination R^2 of the prediction. \n",
        "# The best possible score is 1.0 (58.78% of the variance for assists is explained by the field goals players made)\n",
        "lr_confidence = lr.score(x_test, y_test)\n",
        "print(\"lr confidence (R^2): \", lr_confidence)\n",
        "\n",
        "# mean squared error which tells you how close a regression line is to a set of points.\n",
        "from sklearn.metrics import mean_squared_error\n",
        "print(\"Mean Squared Error (MSE): \",mean_squared_error(y_test, predictions))"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "lr confidence (R^2):  0.5877700445514937\n",
            "Mean Squared Error (MSE):  5257.168578022499\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}
